Instructions to use scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 215 Bytes
1a456b2 | 1 2 3 4 5 6 7 8 9 10 | {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": false,
"sampling_rate": 16000
}
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